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<table width="100%" summary="page for loomis"><tr><td>loomis</td><td align="right">R Documentation</td></tr></table>

<h2>loomis</h2>

<h3>Description</h3>


<p>Data are taken from Loomis (2003). The study relates to a survey taken on reported 
frequency of visits to national parks during the year. The survey was taken at park 
sites, thus incurring possible effects of endogenous stratification.  
</p>


<h3>Usage</h3>

<pre>data(loomis)</pre>


<h3>Format</h3>


<p>A data frame with 410 observations on the following 11 variables.
</p>

<dl>
<dt><code>anvisits</code></dt><dd><p>number of annual visits to park</p>
</dd>
<dt><code>gender</code></dt><dd><p>1=male;0=female</p>
</dd>
<dt><code>income</code></dt><dd><p>income in US dollars per year, categorical: 4 levels</p>
</dd>
<dt><code>income1</code></dt><dd><p>&lt;=$25000</p>
</dd>
<dt><code>income2</code></dt><dd><p>&gt;$25000 - $55000</p>
</dd>
<dt><code>income3</code></dt><dd><p>&gt;$55000 - $95000</p>
</dd>
<dt><code>income4</code></dt><dd><p>&gt;$95000</p>
</dd>
<dt><code>travel</code></dt><dd><p>travel time, categorical: 3 levels</p>
</dd>
<dt><code>travel1</code></dt><dd><p>&lt;.25 hrs</p>
</dd>
<dt><code>travel2</code></dt><dd><p>&gt;=.25 - &lt;4 hrs</p>
</dd>
<dt><code>travel3</code></dt><dd><p>&gt;=4 hrs</p>
</dd>
</dl>



<h3>Details</h3>


<p>loomis is saved as a data frame.
Count models typically use anvisits as response variable. 0 counts are included
</p>


<h3>Source</h3>


<p>from Loomis (2003)
</p>


<h3>References</h3>


<p>Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press
Loomis, J. B. (2003). Travel cost demand model based river recreation benefit 
estimates with on-site and household surveys: Comparative results and a 
correction procedure, Water Resources Research, 39(4): 1105
</p>


<h3>Examples</h3>

<pre>
data(loomis)
glmlmp &lt;- glm(anvisits ~ gender + factor(income) + factor(travel), family=poisson, data=loomis)
summary(glmlmp)
exp(coef(glmlmp))
library(MASS)
glmlmnb &lt;- glm.nb(anvisits ~ gender + factor(income) + factor(travel), data=loomis)
summary(glmlmnb)
exp(coef(glmlmnb))
</pre>


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